Recently there has been a resurgence of learning-based methods for localization. Approaches such as PoseNet [1]} and VLocNet [2]} perform visual localization by training a convolutional neural network to regress to scene coordinates, given an image [1]} or a sequence of images[2]}. Recently, differentiable particle filters (DPFs) have been proposed for global localization [5]}, [6]}. However, such approaches need precisely annotated data to train a PoseNet, VLocnet, or a DPF for each new deployed environment. In contrast, DAL transfers essentially zero-shot across simulated environments, and from a simulator to the real-world.
